SoroushVahidi/scidocs huggingface dataset:
Shot boundary detection from videos using entropy and local descriptor
(2011)

Video shot segmentation is an important step in key frame selection, video copy detection, video summarization, and video indexing for retrieval. Although some types of video data, e.g., live sports coverage, have abrupt shot boundaries that are easy to identify using simple heuristics, it is much more difficult to identify shot boundaries in other types such as cinematic movies. We propose an algorithm for shot boundary detection able to accurately identify not only abrupt shot boundaries, but also the fade-in and fade-out boundaries typical of cinematic movies. The algorithm is based on analysis of changes in the entropy of the gray scale intensity over consecutive frames and analysis of correspondences between SURF features over consecutive frames. In an experimental evaluation on the TRECVID-2007 shot boundary test set, the algorithm achieves substantial improvements over state of the art methods, with a precision of 97.8% and a recall of 99.3%.
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
(2018)

Unsupervised learning for visual perception of 3D geometry is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable progress on geometry perception; however, they perform poorly on dynamic objects and scenarios with dark and noisy environments. In contrast, supervised learning algorithms, which are robust, require large labeled geometric dataset. This paper introduces SIGNet, a novel framework that provides robust geometry perception without requiring geometrically informative labels. Specifically, SIGNet integrates semantic information to make unsupervised robust geometric predictions for dynamic objects in low lighting and noisy environments. SIGNet is shown to improve upon the state-of-the-art unsupervised learning for geometry perception by 30% (in squared relative error for depth prediction). In particular, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction.
Analysis of hidden units in a layered network trained to classify sonar targets
(1988)

-A neural network learning procedure has been applied to the classification ~/sonar returns [kom two undersea targets, a metal cylinder and a similarly shaped rock. Networks with an intermediate layer ~/ hidden processing units achieved a classification accuracy as high as 100% on a training set of l04 returns. These net~orks correctly classified up to 90.4% of 104 test returns not contained in the training set. This perfi~rmance was better than that of a nearest neighbor classifier, which was 82.7%. and was close to that of an optimal Bayes classifie~ Specific signal features extracted by hidden units in a trained network were identified and related to coding schemes in the pattern of connection strengths between the input and the hidden units. Network perlbrmance and class[/~cation strategy was comparable to that of trained human listeners. Keywords--Learning algorithms, Hidden units. Multilayered neural network, Sonar, Signal processing.
Pushover Analysis of 4 Storey ’ s Reinforced Concrete Building
(2013)

The earthquakes in the Indian subcontinent have led to an increase in the seismic zoning factor over many parts of the country. Also, ductility has become an issue for all building that was designed and detailed using earlier versions of the codes. Under such circumstances, seismic qualification of building has become extremely important. The structural engineering profession has been using the nonlinear static procedure (NSP) or pushover analysis. Modeling for such analysis requires the determination of the nonlinear properties of each component in the structure, quantified by strength and deformation capacities, which depend on the modeling assumptions. Pushover analysis is carried out for either user-defined nonlinear hinge properties or default-hinge properties, available in some programs based on the FEMA-356 and ATC-40 guidelines. This paper aims to evaluate the zone –IV selected reinforced concrete building to conduct the non-linear static analysis (Pushover Analysis). The pushover analysis shows the pushover curves, capacity spectrum, plastic hinges and performance level of the building. The non-linear static analysis gives better understanding and more accurate seismic performance of buildings of the damage or failure element.
A Low-Profile Wideband Substrate-Integrated Waveguide Cavity-Backed E-Shaped Patch Antenna for the Q-LINKPAN Applications
(2017)

A low-profile substrate-integrated waveguide cavity-backed E-shaped patch antenna is proposed for the Q-LINKPAN application in this paper. In order to expand the operating bandwidth, a co-planar waveguide (CPW) is used to feed the proposed antenna with a metallized via for generating one more resonant mode. In addition, a substrate-integrated cavity is employed to suppress the surface wave and improve the radiation efficiency. A differential feeding network is used in the design to improve the symmetry of the E-plane radiation pattern and the H-plane cross polarization. A  $2 \times 2$  prototype is designed, fabricated, and measured for a demonstration. The measured results show that the prototype has a 10 dB impedance bandwidth of 34.4%, a gain of around 12.5 dBi with a narrow E-plane radiation beam within 37.5–46 GHz for long distance applications, and a gain of around 8 dBi with a broad E-plane radiation beam within 47–53 GHz for short distance applications. The proposed technique can be used to develop compact planar antenna for meeting both the short- and long-rang communication requirements of the emerging Q-LINKPAN wireless system.
Knowledge Collaboration in Online Communities
(2011)

O communities (OCs) are a virtual organizational form in which knowledge collaboration can occur in unparalleled scale and scope, in ways not heretofore theorized. For example, collaboration can occur among people not known to each other, who share different interests and without dialogue. An exploration of this organizational form can fundamentally change how we theorize about knowledge collaboration among members of organizations. We argue that a fundamental characteristic of OCs that affords collaboration is their fluidity. This fluidity engenders a dynamic flow of resources in and out of the community—resources such as passion, time, identity, social disembodiment of ideas, socially ambiguous identities, and temporary convergence. With each resource comes both a negative and positive consequence, creating a tension that fluctuates with changes in the resource. We argue that the fluctuations in tensions can provide an opportunity for knowledge collaboration when the community responds to these tensions in ways that encourage interactions to be generative rather than constrained. After offering numerous examples of such generative responses, we suggest that this form of theorizing—induced by online communities—has implications for theorizing about the more general case of knowledge collaboration in organizations.
IP Lookup using Two-level Indexing and B-Trees
(2010)

Networks are expanding very fast and the number of clients is increasing dramatically, this causes the router forwarding table to become very large and present more demand on faster router operations. In this paper, we address the problem of packet forwarding in the routers aiming to increase the speed of address lookup and minimize the memory required for storing the forwarding table. We propose a new algorithm that makes use of two-level indexing and Btrees. We test the approach and compare it to other famous IP lookup approaches. The preliminary simulations show 20% less memory requirements and the lookup speed scaling linearly with increasing table size.
mTOR Signaling in Growth Control and Disease
(2012)

The mechanistic target of rapamycin (mTOR) signaling pathway senses and integrates a variety of environmental cues to regulate organismal growth and homeostasis. The pathway regulates many major cellular processes and is implicated in an increasing number of pathological conditions, including cancer, obesity, type 2 diabetes, and neurodegeneration. Here, we review recent advances in our understanding of the mTOR pathway and its role in health, disease, and aging. We further discuss pharmacological approaches to treat human pathologies linked to mTOR deregulation.
Counterintuitive Behavior of Social Systems
(1971)

This paper addresses several social concerns: population trends; quality of urban life; policies for urban growth; and the unexpected, ineffective, or detrimental results often generated by government programs. Society becomes frustrated as repeated attacks on deficiencies in social systems lead only to worse symptoms. Legislation is debated and passed with great hope, but many programs prove to be ineffective. Results are often far short of expectations. Because dynamic behavior of social systems is not understood, government programs often cause exactly the reverse of desired results. The field of system dynamics now can explain how such contrary results happen. Fundamental reasons cause people to misjudge behavior of social systems. Orderly processes in creating human judgment and intuition lead people to wrong decisions when faced with complex and highly interacting systems. Until we reach a much better public understanding of social systems, attempts to develop corrective programs for social troubles will continue to be disappointing. This paper cautions against continuing to depend on the same past approaches that have led to present feelings of frustration. New methods developed over the last 30 years will lead to a better understanding of social systems and thereby to more effective policies for guiding the future. 1 Updated March, 1995. This paper was first copyrighted © 1971 by Jay W. Forrester. It is based on testimony for the Subcommittee on Urban Growth of the Committee on Banking and Currency, U.S. House of Representatives, on October 7, 1970. The original text appeared in the January, 1971, issue of the Technology Review published by the Alumni Association of the Massachusetts Institute of Technology. All figures are taken from World Dynamics by Jay W. Forrester, Productivity Press, Portland, Oregon. 2 Germeshausen Professor Emeritus and Senior Lecturer, Massachusetts Institute of Technology, Cambridge, MA, USA Copyright © 1995 Jay W. Forrester
Overview of narrowband IoT in LTE Rel-13
(2016)

In 3GPP Rel-13, a narrowband system, named Narrowband Internet of Things (NB-IoT), has been introduced to provide low-cost, low-power, wide-area cellular connectivity for the Internet of Things. This system, based on Long Term Evolution (LTE) technology, supports most LTE functionalities albeit with essential simplifications to reduce device complexity. Further optimizations to increase coverage, reduce overhead and reduce power consumption while increasing capacity have been introduced as well. The design objectives of NB-IoT include low-complexity devices, high coverage, long device battery life, and massive capacity. Latency is relaxed although a delay budget of 10 seconds is the target for exception reports. This paper provides an overview of NB-IoT design, including salient features from the physical and higher layers. Illustrative results with respect to performance objectives are also provided. Finally, NB-IoT enhancements in LTE Rel-14 are briefly outlined.
Parenting stress in mothers and fathers of toddlers with autism spectrum disorders: associations with child characteristics.
(2008)

Elevated parenting stress is observed among mothers of older children with autism spectrum disorders (ASD), but little is known about parents of young newly-diagnosed children. Associations between child behavior and parenting stress were examined in mothers and fathers of 54 toddlers with ASD (mean age = 26.9 months). Parents reported elevated parenting stress. Deficits/delays in children's social relatedness were associated with overall parenting stress, parent-child relationship problems, and distress for mothers and fathers. Regulatory problems were associated with maternal stress, whereas externalizing behaviors were associated with paternal stress. Cognitive functioning, communication deficits, and atypical behaviors were not uniquely associated with parenting stress. Clinical assessment of parental stress, acknowledging differences in parenting experiences for mothers and fathers of young children with ASD, is needed.
Dynamic time warping constraint learning for large margin nearest neighbor classification
(2011)

Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin. In addition, a reduction technique is combined with a search process to condense the prototypes. The approach is implemented and tested on UCR datasets. Experimental results show the effectiveness of the proposed method. ! 2011 Elsevier Inc. All rights reserved.
DreamSketch: Early Stage 3D Design Explorations with Sketching and Generative Design
(2017)

We present DreamSketch, a novel 3D design interface that combines the free-form and expressive qualities of sketching with the computational power of generative design algorithms. In DreamSketch, a user coarsely defines the problem by sketching the design context. Then, a generative design algorithm produces multiple solutions that are augmented as 3D objects in the sketched context. The user can interact with the scene to navigate through the generated solutions. The combination of sketching and generative algorithms enables designers to explore multiple ideas and make better informed design decisions during the early stages of design. Design study sessions with designers and mechanical engineers demonstrate the expressive nature and creative possibilities of DreamSketch.
Practical Bayesian Optimization of Machine Learning Algorithms
(2012)

The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a “black art” requiring expert experience, rules of thumb, or sometimes bruteforce search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm’s generalization performance is modeled as a sample from a Gaussian process (GP). We show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance. We describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.
Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid
(1996)

Naive Bayes induction algorithms were previously shown to be surprisingly accurate on many classi cation tasks even when the conditional independence assumption on which they are based is violated How ever most studies were done on small databases We show that in some larger databases the accuracy of Naive Bayes does not scale up as well as decision trees We then propose a new algorithm NBTree which in duces a hybrid of decision tree classi ers and Naive Bayes classi ers the decision tree nodes contain uni variate splits as regular decision trees but the leaves contain Naive Bayesian classi ers The approach re tains the interpretability of Naive Bayes and decision trees while resulting in classi ers that frequently out perform both constituents especially in the larger databases tested
Bayesian Optimization with Robust Bayesian Neural Networks
(2016)

Bayesian optimization is a prominent method for optimizing expensive-to-evaluate black-box functions that is widely applied to tuning the hyperparameters of machine learning algorithms. Despite its successes, the prototypical Bayesian optimization approach – using Gaussian process models – does not scale well to either many hyperparameters or many function evaluations. Attacking this lack of scalability and flexibility is thus one of the key challenges of the field. We present a general approach for using flexible parametric models (neural networks) for Bayesian optimization, staying as close to a truly Bayesian treatment as possible. We obtain scalability through stochastic gradient Hamiltonian Monte Carlo, whose robustness we improve via a scale adaptation. Experiments including multi-task Bayesian optimization with 21 tasks, parallel optimization of deep neural networks and deep reinforcement learning show the power and flexibility of this approach.
Taking the Human Out of the Loop: A Review of Bayesian Optimization
(2016)

Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.
Efficient and Robust Automated Machine Learning
(2015)

The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. Building on this, we introduce a robust new AutoML system based on scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub AUTO-SKLEARN, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won the first phase of the ongoing ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of AUTO-SKLEARN.
The biology of cancer: metabolic reprogramming fuels cell growth and proliferation.
(2008)

Cell proliferation requires nutrients, energy, and biosynthetic activity to duplicate all macromolecular components during each passage through the cell cycle. It is therefore not surprising that metabolic activities in proliferating cells are fundamentally different from those in nonproliferating cells. This review examines the idea that several core fluxes, including aerobic glycolysis, de novo lipid biosynthesis, and glutamine-dependent anaplerosis, form a stereotyped platform supporting proliferation of diverse cell types. We also consider regulation of these fluxes by cellular mediators of signal transduction and gene expression, including the phosphatidylinositol 3-kinase (PI3K)/Akt/mTOR system, hypoxia-inducible factor 1 (HIF-1), and Myc, during physiologic cell proliferation and tumorigenesis.
Plantar fasciitis: evidence-based review of diagnosis and therapy.
(2005)

Plantar fasciitis causes heel pain in active as well as sedentary adults of all ages. The condition is more likely to occur in persons who are obese or in those who are on their feet most of the day. A diagnosis of plantar fasciitis is based on the patient's history and physical findings. The accuracy of radiologic studies in diagnosing plantar heel pain is unknown. Most interventions used to manage plantar fasciitis have not been studied adequately; however, shoe inserts, stretching exercises, steroid injection, and custom-made night splints may be beneficial. Extracorporeal shock wave therapy may effectively treat runners with chronic heel pain but is ineffective in other patients. Limited evidence suggests that casting or surgery may be beneficial when conservative measures fail.
Penetration state transition analysis: A rule-based intrusion detection approach
(1992)

| This paper presents a new approach to representing and detecting computer penetrations in real-time. The approach, called state transition analysis, models penetrations as a series of state changes that lead from an initial secure state to a target compromised state. State transition diagrams, the graphical representation of penetrations, identify precisely the requirements for and the compromise of a penetration and present only the critical events that must occur for the successful completion of the penetration. State transition diagrams are written to correspond to the states of an actual computer system, and these diagrams form the basis of a rule-based expert system for detecting penetrations, called the State Transition Analysis Tool (STAT). The design and implementation of a UNIX-speci c prototype of this expert system, called USTAT, is also presented. This prototype provides a further illustration of the overall design and functionality of this intrusion detection approach. Lastly, STAT is compared to the functionality of comparable intrusion detection tools. Keywords|Security, Intrusion Detection, Expert Systems
Detection and counting of pothole using image processing techniques
(2016)

Pothole are the primary cause of accidents, hence identification and classification using image processing techniques is very important. In this paper image pre-processing based on difference of Gaussian-Filtering and clustering based image segmentation methods are implemented for better results. From the results the K-Means clustering based segmentation was preferred for its fastest computing time and edge detection based segmentation is preferred for its specificity. The main goal of this paper is to identify a better method which is highly efficient and accurate compared to the conventional methods. Different image pre-processing and segmentation methods for pothole detection where reviewed using performance measures.